Vorpal starts with multi-discipline, proprietary data inputs
Building robust strategies requires robust, proprietary data. At the heart of Vorpal Core are eight subsystems that create algorithms from different data types. These subsystems talk to each other and give color and context to the strategies.
AI optimization at each level
Vorpal uses an AI reinforced-learning process at each level of the Vorpal Core to discovery and optimize alphas, strategies and the assets to which they are applied. Vorpal Edge, the front end of our system, includes an AI-learning window that allows the portfolio manager to track the progress of this autonomous optimization over time.
Supervised training by domain experts
We’ve seen AI deployed in a number of industries, from medical imaging to autonomous driving. The key to its success in these fields has been supervised training by domain experts. Computer engineers enlist the help of medical doctors to train the AI systems that look for abnormalities in x-rays and scans. Human drivers assist autopilots in cars to improve performance. Vorpal follows the same formula by inputting our team’s deep domain knowledge of financial markets into the AI system.